Image Segmentation Based on Visual Attention Mechanism

Zhang, Qiaorong; Gu, Guochang; Xiao, Huimin

doi:10.4304/jmm.4.6.363-370

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…Top-down attention mechanism is so subjective that it is very difficult to model this processing mechanism in detail [3] . On the other hand, with bottom-up processing, the human visual system determines salient regions which are obtained from features that are based on the basic information of input image such as intensity, color and orientation etc.…”

Section: Visual Attention Modelsmentioning

confidence: 99%

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A new efficient method for color image compression based on visual attention mechanism

Shao

Gao

et al. 2010

Optoelectronic Imaging and Multimedia Technology

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One of the key procedures in color image compression is to extract its region of interests (ROIs) and evaluate different compression ratios. A new non-uniform color image compression algorithm with high efficiency is proposed in this paper by using a biology-motivated selective attention model for the effective extraction of ROIs in natural images. When the ROIs have been extracted and labeled in the image, the subsequent work is to encode the ROIs and other regions with different compression ratios via popular JPEG algorithm. Furthermore, experiment results and quantitative and qualitative analysis in the paper show perfect performance when comparing with other traditional color image compression approaches.

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Section: Visual Attention Modelsmentioning

confidence: 99%

“…During the research, people find that human have the remarkable ability to find the objects of interest in complex scenes quickly which is called "Visual Attention" (VA) [3] . Human visual attention system can focus on an attentive location in an input scene and select interesting visual information to process in the brain.…”

Section: Introductionmentioning

confidence: 99%

A new efficient method for color image compression based on visual attention mechanism

Shao

Gao

et al. 2010

Optoelectronic Imaging and Multimedia Technology

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“…Scientists took considerable time and efforts to study the human visual attention system [9] in order to segment the targets automatically. Saliency map was firstly introduced by Koch and Ullman.…”

Section: Introductionmentioning

confidence: 99%

Color Image Segmentation in RGB Color Space Based on Color Saliency

Zhang

Yang

Liu

et al. 2014

Computer and Computing Technologies in Agriculture VII

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Abstract. It is difficult to separate the foreign fiber objects in a captured color image from the background accurately. To solve this problem, this paper presents a new approach for color image segmentation in RGB color space based on color saliency. Firstly, the captured RGB color image was separated to R, G and B color channels, and the color information for each channel was calculated. Secondly, the R saliency for each pixel in the R channel, the G saliency for each pixel in the G channel, and the B saliency for each pixel in the B channel was calculated respectively. Then comprehensive saliency map was obtained by the weighted R, G and B saliency. The weights for the R, G and B saliency were determined by the corresponding color information of each color channel. At last, the foreign fiber targets were separated out from the comprehensive saliency map using a threshold method. The results indicate that the proposed method can segment out the foreign fiber objects from the color image accurately.

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“…As such, event-driven systems are inherently more efficient because they acquire, transmit and perform computation only when and where a change in the input has been detected, removing redundancies at the lowest possible level. Selective attention is a key component of artificial sensory systems; in robotics, it is the basis for object segmentation (Qiaorong et al, 2009), recognition (Miau et al, 2001; Walther et al, 2005) and tracking (Ouerhani et al, 2005), for scene understanding and action selection for visual tracking and object manipulation. It is also used in navigation, for self-localization and simultaneous localization and mapping (SLAM) (Frintrop and Jensfelt, 2008).…”

Section: Introductionmentioning

confidence: 99%

Event-driven visual attention for the humanoid robot iCub

2013

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Fast reaction to sudden and potentially interesting stimuli is a crucial feature for safe and reliable interaction with the environment. Here we present a biologically inspired attention system developed for the humanoid robot iCub. It is based on input from unconventional event-driven vision sensors and an efficient computational method. The resulting system shows low-latency and fast determination of the location of the focus of attention. The performance is benchmarked against an instance of the state of the art in robotics artificial attention system used in robotics. Results show that the proposed system is two orders of magnitude faster that the benchmark in selecting a new stimulus to attend.

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Image Segmentation Based on Visual Attention Mechanism

Abstract: A new approach for image segmentation based on visual attention mechanism is proposed. Motivated biologically, this approach simulates the bottom-up human visual selective attention mechanism, extracts Show more

Cited by 14 publications

References 15 publications

A new efficient method for color image compression based on visual attention mechanism

A new efficient method for color image compression based on visual attention mechanism

Color Image Segmentation in RGB Color Space Based on Color Saliency

Event-driven visual attention for the humanoid robot iCub

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